Breast thermography captures infrared radiation images to monitor skin surface temperature changes non-invasively. This data, when combined with artificial intelligence, facilitates early breast cancer diagnosis and detection. However, training deep learning algorithms such as convolutional neural networks is challenging due to the limited number of images. The primary objective of this study is to create a set of synthetic breast thermographic images using segmentation and data augmentation techniques. In this work, we propose 1) Using public breast thermography databases, 2) Segmenting the region of interest with the U-Net network, 3) Increasing the variety of thermographic images using the SNGAN model, and 4) Evaluating the performance and accuracy of the previous algorithms with statistical metrics. The results indicate that the U-Net achieved an IoU of 0.96 and a Dice coefficient of 0.97. The SNGAN network generated 2000 synthetic images, reflected in a KID value of 4.54. In conclusion, U-Net is highly effective for segmenting regions of interest in thermographic images, and SNGAN shows promising results in synthetic image generation.